VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912393852878848 |
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| author | Ling, Zeyu Han, Bo Li, Shiyang Cheng, Jikang Shen, Hongdeng Zou, Changqing |
| author_facet | Ling, Zeyu Han, Bo Li, Shiyang Cheng, Jikang Shen, Hongdeng Zou, Changqing |
| contents | Large language models (LLMs) are, by design, inherently capable of multi-task learning: through a unified next-token prediction paradigm, they can naturally address a wide variety of downstream tasks. Prior work in the motion domain has demonstrated some generality by adapting LLMs via a Motion Tokenizer coupled with an autoregressive Transformer to generate and understand human motion. However, this generality remains limited in scope and yields only modest performance gains. We introduce VersatileMotion, a unified multimodal motion LLM that combines a novel motion tokenizer, integrating VQ-VAE with flow matching, and an autoregressive transformer backbone to seamlessly support at least nine distinct motion-related tasks. VersatileMotion is the first method to handle single-agent and multi-agent motions in a single framework and enable cross-modal conversion between motion, text, music, and speech, achieving state-of-the-art performance on seven of these tasks. Each sequence in MotionHub may include one or more of the following annotations: natural-language captions, music or audio clips, speech transcripts, and multi-agent interaction data. To facilitate evaluation, we define and release benchmark splits covering nine core tasks. Extensive experiments demonstrate the superior performance, versatility, and potential of VersatileMotion as a foundational model for future understanding and generation of motion. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_17335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension Ling, Zeyu Han, Bo Li, Shiyang Cheng, Jikang Shen, Hongdeng Zou, Changqing Computer Vision and Pattern Recognition Large language models (LLMs) are, by design, inherently capable of multi-task learning: through a unified next-token prediction paradigm, they can naturally address a wide variety of downstream tasks. Prior work in the motion domain has demonstrated some generality by adapting LLMs via a Motion Tokenizer coupled with an autoregressive Transformer to generate and understand human motion. However, this generality remains limited in scope and yields only modest performance gains. We introduce VersatileMotion, a unified multimodal motion LLM that combines a novel motion tokenizer, integrating VQ-VAE with flow matching, and an autoregressive transformer backbone to seamlessly support at least nine distinct motion-related tasks. VersatileMotion is the first method to handle single-agent and multi-agent motions in a single framework and enable cross-modal conversion between motion, text, music, and speech, achieving state-of-the-art performance on seven of these tasks. Each sequence in MotionHub may include one or more of the following annotations: natural-language captions, music or audio clips, speech transcripts, and multi-agent interaction data. To facilitate evaluation, we define and release benchmark splits covering nine core tasks. Extensive experiments demonstrate the superior performance, versatility, and potential of VersatileMotion as a foundational model for future understanding and generation of motion. |
| title | VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.17335 |